HW5 - Make Data Move
Katrine
17/10/2020
Explore the recent global developments with R
Today, you will load a filtered gapminder dataset - with a subset of data on global development from 1952 - 2007 in increments of 5 years - to capture the period between the Second World War and the Global Financial Crisis.
Your task: Explore the data and visualise it in both static and animated ways, providing answers and solutions to 7 questions/tasks below.
Get the necessary packages
First, start with installing the relevant packages ‘tidyverse’, ‘gganimate’, and ‘gapminder’.
## ── Attaching packages ────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.2
## ✓ tidyr 1.1.1 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ───────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()Look at the data
First, see which specific years are actually represented in the dataset and what variables are being recorded for each country. Note that when you run the cell below, Rmarkdown will give you two results - one for each line - that you can flip between.
unique(gapminder$year)## [1] 1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 2002 2007head(gapminder)## # A tibble: 6 x 6
## country continent year lifeExp pop gdpPercap
## <fct> <fct> <int> <dbl> <int> <dbl>
## 1 Afghanistan Asia 1952 28.8 8425333 779.
## 2 Afghanistan Asia 1957 30.3 9240934 821.
## 3 Afghanistan Asia 1962 32.0 10267083 853.
## 4 Afghanistan Asia 1967 34.0 11537966 836.
## 5 Afghanistan Asia 1972 36.1 13079460 740.
## 6 Afghanistan Asia 1977 38.4 14880372 786.The dataset contains information on each country in the sampled year, its continent, life expectancy, population, and GDP per capita.
Let’s plot all the countries in 1952.
theme_set(theme_bw()) # set theme to white background for better visibility
ggplot(subset(gapminder, year == 1952), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() 
We see an interesting spread with an outlier to the right. Answer the following questions, please:
#Q1. Why does it make sense to have a log10 scale on x axis?
We have a very big amount of data and therefore it’s reasonable to use the log10 scale, which spends over very big numbers. R is set for scientific notation.
#Q2. What country is the richest in 1952 (far right on x axis)?
#I create a pipe using the filter-, select- and arange-functions:
gapminder %>%
filter(year == 1952) %>%
select (country, gdpPercap) %>%
arrange(desc(gdpPercap))## # A tibble: 142 x 2
## country gdpPercap
## <fct> <dbl>
## 1 Kuwait 108382.
## 2 Switzerland 14734.
## 3 United States 13990.
## 4 Canada 11367.
## 5 New Zealand 10557.
## 6 Norway 10095.
## 7 Australia 10040.
## 8 United Kingdom 9980.
## 9 Bahrain 9867.
## 10 Denmark 9692.
## # … with 132 more rows#filter() gives me the rows on the condition that the year = 1952 #select() tell R to only list the coloumns “country” and "gdpPercap #finally, arrange(desc) tells R to list the input (gdpPercap) in descending order.
#From the output of the pipeline, I can tell that Kuwait was the richest country in 1952 with a gdpPercap on 108382.
You can generate a similar plot for 2007 and compare the differences
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() 
The black bubbles are a bit hard to read, the comparison would be easier with a bit more visual differentiation.
#Q3. Can you differentiate the continents by color and fix the axis labels?
#I add the aes(color)-function within the geom_point in order to make the continents different colors and in the end I use the labs-function in order to give the axis better names:
ggplot(subset(gapminder, year == 2007), aes(gdpPercap, lifeExp, size = pop)) +
geom_point(aes(color = continent)) +
scale_x_log10() +
labs(x="GDP per capita", y = "Expected lifetime")
#Q4. What are the five richest countries in the world in 2007?
gapminder %>%
filter(year == 2007) %>%
select (country, gdpPercap) %>%
arrange(desc(gdpPercap)) %>%
head(5)## # A tibble: 5 x 2
## country gdpPercap
## <fct> <dbl>
## 1 Norway 49357.
## 2 Kuwait 47307.
## 3 Singapore 47143.
## 4 United States 42952.
## 5 Ireland 40676.#I can reuse the code used to answer Q2, changing the “year == 1952” to “year == 2007”. However, I add head(5) as well in order to make R give me the five first results, thereby telling me the five richest countries in the world in 2007: Norway (gdpPercap = 49357), Kuwait (gdpPercap = 47307), Singapore (gdpPercap = 47143), United States (gdpPercap = 42952) and Ireland (gdpPercap = 40676)
Make it move!
The comparison would be easier if we had the two graphs together, animated. We have a lovely tool in R to do this: the gganimate package. And there are two ways of animating the gapminder ggplot.
Option 1: Animate using transition_states()
The first step is to create the object-to-be-animated
# Installing and loading packages needed:
#install.packages("gifski")
#install.packages("png")
library(gifski)
library(png)
anim <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() # convert x to log scale
anim
This plot collates all the points across time. The next step is to split it into years and animate it. This may take some time, depending on the processing power of your computer (and other things you are asking it to do). Beware that the animation might appear in the ‘Viewer’ pane, not in this rmd preview. You need to knit the document to get the viz inside an html file.
anim + transition_states(year,
transition_length = 1,
state_length = 1)
Notice how the animation moves jerkily, ‘jumping’ from one year to the next 12 times in total. This is a bit clunky, which is why it’s good we have another option.
Option 2 Animate using transition_time()
This option smoothes the transition between different ‘frames’, because it interpolates and adds transitional years where there are gaps in the timeseries data.
anim2 <- ggplot(gapminder, aes(gdpPercap, lifeExp, size = pop)) +
geom_point() +
scale_x_log10() + # convert x to log scale
transition_time(year)
anim2
The much smoother movement in Option 2 will be much more noticeable if you add a title to the chart, that will page through the years corresponding to each frame.
#Q5 Can you add a title to one or both of the animations above that will change in sync with the animation? [hint: search labeling for transition_states() and transition_time() functions respectively]
#I look up how to label animations at the cheat-sheet for "Animate ggplots withgganimate" (https://ugoproto.github.io/ugo_r_doc/pdf/gganimate.pdf)
#For transition_states(), I have to use the "labs(subtitle = "{next_state}")-function:
anim + labs(subtitle = "GDP per capita in {next_state}") + transition_states(year,
transition_length = 1,
state_length = 1)
#For transition_time, I have to use the "labs(subtitle = "{frame_time}")-function:
anim2 + labs(subtitle = "GDP per capita in {frame_time}") 
#Q6 Can you made the axes’ labels and units more readable? Consider expanding the abreviated lables as well as the scientific notation in the legend and x axis to whole numbers.[hint:search disabling scientific notation]
I add the labels for x and y within the labs()-function. By using options(scien=999), I can force R to diable scientific notation, whereby making the numbers in the x-asis whole numbers (https://stackoverflow.com/questions/5352099/how-to-disable-scientific-notation):
options(scipen=999)
anim2 + labs(x="GDP per capita", y = "Expected lifetime", subtitle = "GDP per capita in {frame_time}")